Tag Archives: surprise loyalty

In my last post I described a set of analytics projects that drive real competitive advantage in retail and eCommerce. These projects are meant to be the opening of the third and final stage of an analytically driven digital transformation. They are big, complex, important projects that make a real difference to the way the business works.

But I know folks outside retail (and they’re the majority of my client-base) get frustrated because so much of the analytics technology and conversation seems to reflect retail concerns. So in this post, I wanted to describe an alternative set of projects specifically for another industry (I picked Hospitality) and talk a little bit about some of the key analytics flashpoints in different industries. Every business is unique. There is no one right set of projects when you get to this phase of digital transformation, but there are analytics projects that are quite important to almost everyone in a given industry.

Here’s a fairly generic set of projects I’d typically attach to a presentation on digital transformation in hospitality. You can see that about half the projects are the same as what I recommended for retail.

Aggressive personalization is a core part of MOST good digital programs – almost regardless of industry. If you’re in health-care, financial services, retail, travel & hospitality, government or technology, then analytics-driven personalization should be a high priority. It’s actually a lot easier to say where personalization might not be near the top of the list: CPG and maybe manufacturing. In CPG, many web sites are too shallow and lack enough interesting content to make personalization effective. In fact, the Website itself is often pretty unimportant. CPG folks should probably be more worried about their marketing and social media analytics than personalization. Manufacturers might be on the same level, but a lot depends on the type of industry, how many products you have, how many audiences, and how much content. In every case, the more you have (product, audience, content), the more likely it is that personalization should be a strategic priority.

I also included Surprise-based Loyalty. Travel is actually the sector where I first developed these concepts. You can read a somewhat more detailed explanation in an article I recently published in the CIO Outlook for Travel & Hospitality. But there are quite a few reasons why hospitality, in particular, is a great place to build a surprise-based program. First, the hospitality industry has numerous opportunities to deliver surprise-based loyalty at little or no cost. That’s critical. Hospitality also has the requisite data to allow for powerful analytic targeting and has sufficient touches to make the concept powerful and workable. What’s more, most of the rewards programs in hospitality suffer from scale. Sure, a few global giants have the reach to make a traditional loyalty program appealing. But if you’re a boutique or mid-tier chain, your traditional loyalty program will never look particularly attractive. Surprise-based concepts get around all that. With no fixed cost, the ability to target and grow them organically, and real impact on loyalty, they deliver a fundamentally different kind of experience that doesn’t depend on scale and global reach.

My third project is another one that could appear almost everywhere: mobile optimization. For Hospitality, it’s particularly important to create a great mobile on-property experience and build out the mobile experience as the Hub for loyalty. Integration of mobile digital experiences with property systems enables a whole array of real experience difference makers – room selection, automatic upgrading, room bidding, expedited check-in, door control, service requests and, of course, plenty of surprise (and traditional) loyalty opportunities.

Why didn’t this show up in retail? Hey, it could. It might be sixth on my generic list. But many of the retailers I’m working with are struggling to figure out how to make mobile an important part of the experience. With all the beaconing and wifi we’ve seen, most opt-in systems simply don’t get enough adoption to make them worthwhile. I think it’s easier to drive adoption in hospitality. And adoption is critical to driving serious advantage.

When I talk about advanced Revenue Management I’m clearly hovering somewhere on the edge of what might reasonably be considered digital. There are lots of different ways to improve revenue management, but what I have in mind here are two specific types of analysis. The first is using digital view volume to feed demand signals into revenue management. This is a simple but effective technique for taking advantage of your digital data to improve your price planning. I also believe that in the zero-sum game that is room (and flight) planning, there are opportunities to use digital data collection from OTAs to reverse engineer competitor pricing strategies and then optimize your price curve to take advantage of that knowledge.

In retail, I talked about the growing importance of electronic signage and integrated digital experiences and optimizing the measurement of those (largely unmeasured) systems. In Hospitality, I’ve picked something that isn’t quite the same but falls in the same omni-channel category – optimizing the integration of on-property with digital. This cuts in both directions and overlaps with the analytics around mobile (obviously), personalization (obviously), surprise-loyalty (obviously) and revenue management. Revenue Management a little less obviously but most revenue management systems use time-based pricing not customer based pricing – often completely missing differentials in customer value from on-property behavior. Casino’s, of course, are the exception to this.

For resort properties, there are significant opportunities to integrate digital view behavior into on-property drives. But for almost every type of property there are ways to make the on-property experience better. Some of this is ridiculously easy. When I log into my hotel wifi, I almost always get the standard property page. No customization. No personalization. But I’m a heavy consumer of certain types of on-property experiences including some highly-profitable ones like late-night room service dinners. Do I ever get a dinner drive? A special offer? A loyalty treat? Nope. Pretty much never.

I put this digital/on-property integration high on the list mostly because when it comes to hospitality, the on-property experience is THE critical factor. I might love or hate the Website or even the App, but both are just little bumps on the great big behind that is the actual stay. If I can help make the stay experience better with digital, I’ve done something important.

So my top five projects for hospitality are:

Personalization

Surprise-based Loyalty

Digital Additions to Revenue Management

Mobile Experience and Loyalty Optimization

On-Property digital integration

As with retail, none are easy. Most involve complex integrations AND deep analytics to work well. But they form a powerful and powerfully related nexus of programs that drive real competitive advantage.

Of course, as I’ve tried to make clear, the selection of a top-five is utterly arbitrary. Every business will have its unique strategic priorities, market position, and brand. Those things matter. What’s more, the third phase of an analytics transformation is open-ended. There aren’t just five things. You don’t stop when (if ever) you’ve done these projects.

So it’s natural to ask what are some other commonly important projects that didn’t make the list (and weren’t already captured in the earlier two phases). Here, with some notes about industries, are some more things to chew on:

Digital Acquisition Optimization (Campaign-level): I’ve already covered both a campaign measurement framework and Mix/Attribution in the first two phases. But I haven’t been quite true to myself since I often tell clients to worry about optimizing your individual channels and campaigns first before you worry about attribution. There are more powerful analytic techniques for campaign-specific optimization than attribution – and many, many enterprises would be well-advised focus on those techniques as part of their overall digital transformation. I won’t say that every digital media buy I see sucks. But a lot do. This one isn’t specific to industry; it’s important to anyone dropping significant dime on digital marketing.

Right-Channeling Support: This analytics project often makes my top-five list in financial services, technology, and health care (but it’s important in a lot of other places too). Not only is the call-center a significant cost for many an enterprise, it’s almost always a significant driver of churn and bad experience. That’s not always because call-centers are bad – it’s hard to do well. And these days, many people (I’m certainly in this bucket) flat out prefer digital servicing in most use-cases. So digital servicing is a big deal and it’s deeply analytic. Bridging digital and call is a huge analytics opportunity and one of the most important projects you can take in a digital transformation.

Digital Sales Support: If a field-sales force is a core part of your business, then digital analytics to support what they do is often in my top-five projects around transformation. Technology, Pharma, and certain areas of Financial Services (like Insurance and Wealth) all need to figure out how their digital assets play with their field sales force. Siloed approaches here are worse than silos in digital marketing attribution. You can NOT do this well unless you tackle it as an integrated effort with consistent measurement across the journey.

Content Attribution: When I was at the Digital Analytics Hub in Europe one of the most interesting parts of the discussion around transformation focused on the need for traditional companies to become, in effect, media companies. There’s nothing terribly original about this idea (not sure who’s it is), but it is terribly important and often it’s a huge stumbling block when it comes to transformation. Companies don’t build nearly enough content to be good at digital and they don’t measure it appropriately. Learning how to measure the content experience and how to take advantage of content are keys to effective digital transformation and anyone focused on building deeper sales cycles should think carefully about making content attribution a prominent part of their initial analytics plan.

Balancing Success: One of the biggest failure points in digital transformation in my client-base involves situations where a digital property has several very important enterprise functions. Selling and generating leads, advertising and engagement, linear vs. direct consumption, building brands vs. generating revenue. These are all common examples. The problem is that most enterprises are wishy-washy when it comes to balancing these objectives. When I ask senior folks what they really want (or when I look at how people are measured), what I usually hear is both. That’s not helpful. There are analytic approaches to measuring the trade-offs in site real-estate and marketing between driving to multiple types of success. If you haven’t done the analytics work to figure this out and set appropriate incentives and performance measurements, you’re simply not going to be good at all – and perhaps any – of your core functions.

Well, I could go on of course. But I’m almost at four pages now – which I know is excessive. There are a lot of options. That’s why creating a strategic plan for analytics transformation isn’t trivial and it isn’t boilerplate. But as I pointed out in my introduction to the last post, this is the fun stuff.

In my next post, I hope to tackle those organizational issues I’ve been deferring for so long – but I may have one or two more detours up my sleeve!

In my last posts before the DA Hub, I described the first two parts of an analytics driven digital transformation. The first part covered the foundational activities that help an organization understand digital and think and decide about it intelligently. Things like customer journey, 2-tiered segmentation, a comprehensive VoC system and a unified campaign measurement framework form the core of a great digital organization. Done well, they will transform the way your organization thinks about digital. But, of course, thinking isn’t enough. You don’t build culture by talking but by doing. In the beginning was the deed. That’s why my second post dealt with a whole set of techniques for making analytics a constant part of the organization’s processes. Experimentation driven by a comprehensive analytics-driven testing plan, attribution and mix modelling, analytic reporting, re-survey, and a regular cadence of analytics driven briefings make continuous improvement a reality. If you take this seriously and execute fully on these first two phases, you will be good at digital. That’s a promise.

But as powerful, transformative and important as these first two phases are, they still represent only a fraction of what you can achieve with analytics driven-transformation. The third phase of analytics driven transformation targets areas where analytics changes the way a business operates, prices its products, communicates with and supports its customers.

The third phase of digital transformation is unique. In some ways, it’s easier than the first two phases. It involves much less organization and cultural transformation. If you done those first two phases, you’re already there when it comes to having an analytics culture. On the other hand, in this third phase the analytics projects themselves are often MUCH more complex. This is where we tackle big hard problems. Problems that require big data, advanced statistical analysis, and serious imagination. Well, that’s the fun stuff. Seriously, if you’ve gotten through the first two phases of an analytics transformation successfully, doing the projects in Phase Three is like a taking a victory lap.

There isn’t one single blueprint for the third phase of an analytics driven transformation. The work that gets done in the first two phases is surprisingly similar almost regardless of the industry or specific business. I suppose it’s like laying the foundation for a building. No matter what the building looks like, the concrete block at the bottom is going to look pretty much the same. At this third level, however, we’re above the foundation and what you do will depend mightily on your specific business.

I know that it depends on your business is not much of an answer. As a consultant, it’s not unusual to get caught up in conversations like this:

“So how much would it cost?”

“Well, that depends.”

“What kind of things does it depend on?”

“Well, it depends on how deeply you want to go into it, who you want to have do it, and how you want to get it done.”

All of this is true, of course, but none of it is helpful. I usually try to short-circuit these conversations by presenting a couple of real world alternatives.

I think this is more helpful (though it’s also more dangerous). Similarly, when I present the third phase of an analytics driven transformation I try to make it specific to the business in question. And the more I know about the business, the more pointed, interesting, and – I hope – convincing that third phase is going to look. But if I haven’t spent much time a business, I still customize that third phase by industry – picking out high-level analytics projects that are broadly applicable to everyone in the sector.

That’s what I’m going to try to do here, with the added benefit of picking a couple different industries and showing how the differences play out in this third phase. Do keep in mind, though, that the description of this third phase – unlike that of the first two – is meant to be suggestive only. No real-world third phase (certainly no optimal one) is likely to mirror what I lay out here. It might not even be very close. What’s more, unlike the first phase (at least) which is close-ended (when you’ve done the projects I suggest you’re done with that phase), phase three is open-ended. You never stop doing analytics projects at this level. And that’s a good thing.

For the first example, I decided to start with a classic retail e-commerce view of the world. It’s a sector where we all have, at the very least, a consumer’s understanding of how it works. There are many, many possible projects to choose from, but here are five I often present as a typical starting point.

The first is an analytically driven personalization program. With journey-mapping, 2-tiered segmentation and a robust experimentation program, an enterprise should be a in a good position to drive personalization. Most personalization programs bootstrap themselves by starting with fairly straightforward segmentations (already done) and rule-based personalization decisions targeted to “easy” problems like email offers and returning visitors to the Website. That’s fine. The very best way to build a personalization program is organically – build it by doing it with increasing sophistication in more and more channels and at more and more touchpoints.

Merchandising optimization is another very big opportunity. So much of the merchandising optimization I see is focused on product detail pages. That’s fine as far as it goes, but it misses the much larger opportunity to optimize merchandising on search and aisle pages via analytics. Traditional merchandising folks have been slow to understand how critical moving merchandising upstream is to effective digital performance. This turns out to be analytically both very challenging and very rich.

Assortment optimization (and I might be just as likely to pick pricing or demand signals here) has long been a domain of traditional retail analytics. As such, I have to admit I didn’t think much about it until the last few years. But I’ve come to believe that digital analytics can yield powerful preference information that is typically missing in this analysis. To do effective assortment optimization, you need to understand customer’s potential replacement options. In the offline world, this usually involves making simple guesses based on high-level product sales about which products will be substituted. Using online view data, we can do much, much better. This is a case where digital analytics doesn’t so much replace an existing technique as deepen and enrich it with data heretofore undreamed of. Assortment optimization with digital data gives you highly segmented, localized data about product substitution preferences. It’s a lot better.

I’ve become a strong advocated for a fundamental re-think of loyalty programs based on the idea that surprise-based loyalty with no formal earning system is the future of rewards programs. The advantages of surprise-based loyalty are considerable when stacked up against traditional loyalty programs. You can target rewards where you think they will create lift. You can take advantage of inventory problems or opportunities. You don’t incur ANY financial obligations. You create no customer resentment or class issues. You can scale them and localize them to work with a specially trained staff. And, of course, the biggest bonus of all – you actually create far more impact per dollar spent. Surprise-based loyalty is, inherently, analytic. You can’t really do it any other way. Where it’s an option, it’s always one of the biggest changes you can make in the way your business works.

Finally, I’ve picked digital/store integration as my fifth project for analytics-led transformation. There are a number of different ways to take this. The drives between store and site are complex, important and fruitful. Optimizing those drives should be one of the analytics priorities for any omni-channel retail. And that optimization is a combination of testing and analytics. In this case, however, I’ve chosen to focus on measuring and optimizing digital in-store experiences. You’re surely familiar with endless-aisle retail; where digital is integrated into the in-store experience. The vast majority of these physical-digital experiences have been quite ineffective. Almost always, they’ve been executed from a retail perspective. By which I mean that they’ve been built once, dropped into the store, and left to fail. That’s just not doing it right. In-store experiences are getting more digital. Digital signage is growing rapidly. Physical-digital experiences are increasingly common. But if you want actual competitive advantage out of these experiences, you’d better tackle them from a digital test-and-learn/analytics perspective. Anything less is a prescription for failure.

So here’s my first round of Phase Three projects for an analytics driven transformation in retail. Each is big, complex and hard. They are also important. These are the projects that will truly transform your digital business. They are rubber-meets-the-road stuff that drive competitive advantage. It would be a mistake to try and execute on projects like this without first creating a strong analytics foundation in the organization. You’re chances of misfiring on doing or operationalizing the analytics are simply too great without that foundation. But if you don’t move past the first two phases into analytics like this, you’re missing the big stuff. You can churn out lots of incremental improvement in digital without ever touching projects like these. Those incremental improvements aren’t nothing. They may be valuable enough to justify your time and money. But if that’s all you ever do, you’ll likely find yourself wondering if it was all really worth it. Do any of these projects successfully, and you’ll never ask that question again.

Next week I’ll show a different (non-retail) set of projects and break-down what the differences tell us about how to make analytics a strategic asset.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.